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How Urban Tree Canopy Regulates Microclimate and Urban Heat Islands: A Study from Denver and Baltimore

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Robert Taylor
Mehdi Heris
Austin Troy, PhD
University of Colorado Denver
Presented at the Partners in Community Forestry C...

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Why do we care about urban heat?
http://www.urban-climate-energy.com/urbanHeatIsland.htmEPA

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How does land cover change impact urban
energy budget?
• Absorptivity, reflectivity, transmissivity, and
emissivity.
• Dif...

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How Urban Tree Canopy Regulates Microclimate and Urban Heat Islands: A Study from Denver and Baltimore

  1. 1. Robert Taylor Mehdi Heris Austin Troy, PhD University of Colorado Denver Presented at the Partners in Community Forestry Conference 2015 How Urban Tree Canopy Regulates Microclimate and Urban Heat Islands: A Study from Denver and Baltimore
  2. 2. Why do we care about urban heat? http://www.urban-climate-energy.com/urbanHeatIsland.htmEPA
  3. 3. How does land cover change impact urban energy budget? • Absorptivity, reflectivity, transmissivity, and emissivity. • Different surfaces absorb and reflect different quantities in different wavelengths of the spectrum. Impervious surface absorbs lots of radiation in the short-wave portion • Different surfaces shed heat (emissivity) at different rates • Building canyons can impede release of long- wave energy to atmosphere • Convective heat transfer • Anthropogenic heat • Less evapotranspiration (see next slide)
  4. 4. How do trees work with urban heat? • Evapotranspiration: e.g. 40,000 GPY for large oak. ET cools air by using heat from air to evaporate water and can reduce temp. Effects can be up to 9ºF • Direct shade (we’ll get to this later): in summer only 10-30% of sun’s E reaches area below • Higher emissivity and less stored heat
  5. 5. impervious surface to canopy ratio
  6. 6. North Side South Side 60 42 8 19 6.7 68 7 76 100 40.72 102 8% 19% 6.7% 68% 7% 0.76 -0.99 Colfax Corridor Temperature roof color canopy building area parking area impervious surfaces vacant land CanImp index compactness index 39.46 107.0 12% 20% 0.8% 51% 1% 0.49 -0.90 40 38 12 20.0 1 51 1 50 90 North Side South Side Surface Temperature and Morphology in Denver
  7. 7. Our project: Urban heat at two scales http://thinkgreendegrees.com/wp-content/uploads/2012/08/urban-heat-island-comparison.gif
  8. 8. Research Questions 1. How does the heat island effect differ in a humid temperate city (Baltimore) versus and semi-arid zone city (Denver)? 2. How does the spatial pattern of tree canopy mediate trees’ influence on the heat island effect and how does this effect vary between Denver and Baltimore? 3. How can we calculate the amount of tree shade that directly hits buildings and does the proportion of shaded building area vary between Denver and Baltimore?
  9. 9. Question 1: Heat island in Denver vs. Baltimore
  10. 10. Tree Canopy Comparison Denver has 31.8 square km of tree canopy coverage Baltimore has 48.4 square km of tree canopy coverage
  11. 11. 18 Surface Temperature: Baltimore From ASTER Satellite
  12. 12. Surface Temperature: Denver Nighttime August 2003 Daytime June 2012
  13. 13. Transect analysis 20
  14. 14. trend of building area (vertical axis), distance from center (horizontal axis), average patch area radius of circles), and temperature (color) 21
  15. 15. trend of building area (vertical axis), distance from center (horizontal axis), average patch area radius of circles), and temperature (color) 22
  16. 16. Transect analysis x axis: distance from downtowns 23
  17. 17. 24 Denver Baltimore
  18. 18. Why? • In eastern city surrounded by natural forest, outskirts cool down much quicker at night due to high emissivity relative to city • In semi-arid location, trees are not endemic to outskirts, rather urbanization results in MORE trees than would otherwise be there. • The fact that urbanized areas tend to go along with trees means that heat trapping effect of impervious area is largely offset by increase in tree cover relative to surrounding prairie. • Exceptions: downtown, where tons of building area relative to trees; airport, where lots of impervious area
  19. 19. Question 2: Spatial patterns of trees and urban heat mitigation
  20. 20. Average patch circularity At 500 m scale: • Circularity of patch geometries cirRatio = (math.pi * 4* area) / math.pow (perimeter,2) 27 Patch circularity(blue is low, red is high)
  21. 21. Patch edge/area ratio At 500 m scale: • Average edge/area ratio 28 Patch edge area ratio (blue is low, red is high)
  22. 22. Patch area/patch envelope ratio At 500 m scale: • Average patch area/patch envelope ratio 29 Patch area envelope ratio (blue is low, red is high)
  23. 23. Results of Correlations 30 Temperature 5th Jul 2014 Temperature 29th Jun 2012 Temperature 18th Jun 2005 (nighttime) Temperature 24th Aug 2009 Patch density (number of patches) 0.64 0.57 0.05 0.17 Total patch area -0.39 -0.27 -0.30 0.02 Patch average area -0.28 -0.23 -0.11 -0.03 Patch length 0.39 0.41 -0.12 0.16 Patch circularity 0.38 0.31 0.10 0.12 Patch Length / area ratio 0.44 0.35 0.18 0.11 Patch Envelope Length / width ratio 0.07 0.08 -0.12 0.08 Patch area to envelope area ratio -0.22 -0.14 -0.24 0.08 Moran's I 0.29 0.34 0.44 0.07 Temperature 5th Jul 2014 Temperature 29th Jun 2012 Temperature 18th Jun 2005 (nighttime) Temperature 24th Aug 2009 Road area 0.13 0.17 -0.06 0.06 Water area -0.14 -0.25 0.25 -0.01 Grass area -0.04 0.06 -0.36 0.06 Parking area 0.77 0.63 0.31 0.15 Housing unit 2000 0.64 0.56 0.16 0.12 Housing unit 2010 0.64 0.56 0.17 0.12 Building area 0.82 0.71 0.21 0.16 Tree Canopy area -0.47 -0.27 -0.30 0.02 Patch area to minimum bounding envelope area ratio -0.02 0.05 0.01 -0.05 Patch orientation 0.21 0.15 0.07 0.21 Minimum bounding geometry length to width ratio 0.02 0.01 0.02 0.02 Gray= Nighttime temperature Yellow= Relatively high correlation values Orange= Statistically insignificant correlations
  24. 24. Patch correlation trend analysis Levelling off point around 5000 sq m. 31 we measured correlation of temperature and patch-length/area-ratio when the patch average area increases
  25. 25. Results so far for Baltimore • “Edginess” has less of an impact on increasing temperatures for small patches than large patches of forest; influence increases until 5000 square meter patch size • Effects of tree canopy pattern when controlling for impervious/ built area (R-squared ~ .78): • Patch area decreases temp • Patch density (means more, small patches) increases temp • Patch length increases temp • Patch circularity (associated more with individual crowns) increases temp • Patch edge to area ratio increases temp • Circularity and edge-area results may seem at odds, but they proxy different things 32
  26. 26. Question 3: Quantifying tree shade hitting buildings: Denver vs. Baltimore
  27. 27. Analysis of shade based on LiDAR 6pm Shadow from Buildings & Canopy 34
  28. 28. 35
  29. 29. Shade Effects – Solar Angle & Azimuth
  30. 30. Shade Effects – Integrated over time 9am 6am 12pm 3pm 6pm
  31. 31. Shade Effects – Isolated & CombinedTrees Buildings Trees & Buildings
  32. 32. 3D Shade Approach: 8am
  33. 33. 3D Shade Approach: 9am
  34. 34. Methods 1) Data Processing 2) Insolation Calculations 3) Shade Calculations 4) Insolation-Shade Integration Data Processing Insolation Calculations Shade Calculations Insolation- Shade Calculations Data Processing Statistical Processing Unclassified LiDAR Data Vector Data DEM Buildings DEM Deciduous DEM Coniferous DSM Sun’s Position Data LAI Solar Potential Grid Roof Surface Shade Effect Grid Surface Solar Potential Post-Shade Effect Aggregated Shade Effect per Apt. Unit Resulting Statistical Relationships 1 2 3 4 5 41
  35. 35. Total Shade Comparison 0 100 200 300 400 500 600 ShadeArea[km^2] Hours Denver Shade Area Baltimore Shade Area Total Tree Shade : 4 hour interval (summed) * 3 days (June 15, July 15, August 15) 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 ShadeArea[km^2] Hours Denver Shade Area Baltimore Shade Area
  36. 36. Roof-Tree Intersection Shade Comparison Roof-Tree Intersection Shade : 4 hour interval (summed) * 3 days (June 15, July 15, August 15) 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 11 12 ShadeArea[km^2] Hours Denver Shade Area Baltimore Shade Area 0 5 10 15 20 25 30 35 40 45 ShadeArea[km^2] Hours Denver Shade Area Baltimore Shade Area
  37. 37. Conclusion and next steps • Heat island effect is very different in eastern and western US • Western cities surrounded by treeless lands tend to have more trees relative to natural surroundings than do eastern cities • Spatial pattern of tree canopy has a big impact on heat mitigation • Tree shade varies between Denver and Baltimore: more overall tree shade in Baltimore, but more tree shade hitting buildings in Denver by far • Next step: relate this to energy consumption data
  38. 38. Thanks to the Baltimore Ecosystem Study and the USDA Forest Service’s Northern Research Station and Northeastern Area State and Private Forestry Program for their support of this research Robert Taylor Mehdi Heris Austin Troy, PhD University of Colorado Denver Austin.troy@ucdenver.edu Thanks! Questions?

Editor's Notes

  • Canyons work in two ways: trap radiation through bouncing against surface. Second, canyons can channelize and intensify wind, increases convective heat loss. Canyon effect can mitigate, but bouncing effect increases it. Convective heat transfer: when object heats up, it exchanges heat through air movement. The more complex the objects, the less convective heat loss.
  • Evaporative cooling. Technical: latent heat loss. Veg stores less heat. Has more water in it, so can store as latent heat.
  • Note dotted lines. Surface temperature is more spatially variable than air temperature. Note heterogeneity of surface temps. Surface temperature is a proxy for air temperature.
  • Blue in city due to vegetation
  • Note the turquoise color so close to downtown
  • Aster data. 5 Infra red thermal bands. Denver: used 4 scenes, Baltimore: 4 or 5. 2008-2014. Chose one day, based on best fit. 11 AM Denver. After noon Baltimore.
  • Highways have highest heat storage capacity.
  • Notice small variations can have significant impact on temperature.
  • If can edit, move earlier. Units is square meters. Daytime temps. Butterfly patter, One wing is hot, high impervious low canopy areas. Other is mid impervious but high canopy. Seems like only need small amount of tree cover (1500) to get big temperature impact. This is Denver
  • The canopied neighborhoods are cooler than prairie in daytime, because no shade in prairie. They are cooler than downtown in evening, because absorbed less heat.
  • Patch area is vegetation
  • In day the shaded neighborhoods are cooler than then outskirts
    In the night, the shaded neighborhoods are cooler than downtown. They are moderated.
  • Each cell is 500x500. Change legend to patch circulatity
  • Change legend to edge area ratio
  • Change legend. This ratio refers to how much of envelope the shape fills. Things with low value are more complex and irregular shapes.
  • These are for Baltimore One of the temperature values is for nighttime and is highlighted with gray color. Relatively high correlation values are highlighted with yellow color. Statistically insignificant correlations are highlighted with red color. Allnighttime correlations are significant. Note high patch density is high in day, low at night. Indicates more dispersed and discrete tree cover. All for Baltimore. Similar table for Denver not here. These are univariate correlations
  • Green line is significant
  • Circularity represents small individual trees. Circularity and edge/area ratio results are at odds with each other.
  • Video of lidar scene from far to close to illustrate use of data points
  • Change message on compact. Actually compact associated with more heat.

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